arXiv:2510.03534v5 Announce Type: replace-cross Abstract: We study the problem of long-term (multiple days) mapping of a river plume using multiple autonomous underwater vehicles (AUVs), focusing on the Douro river representative use-case. We propose an energy - and communication - efficient multi-agent reinforcement learning approach in which a central coordinator intermittently communicates with the AUVs, collecting measurements and issuing commands. Our approach integrates spatiotemporal Gaussian process regression (GPR) with a multi-head Q-network controller that regulates direction and sp
The increasing sophistication of multi-agent reinforcement learning combined with the rising urgency of environmental monitoring and resource management makes this development timely.
This research demonstrates a practical application of advanced AI and autonomous systems for long-term environmental monitoring, offering a higher resolution and more energy-efficient approach to understanding critical hydrological systems.
The ability to conduct long-term, energy-efficient environmental mapping using autonomous underwater vehicles shifts from reactive, episodic monitoring to proactive, continuous, and adaptive intelligence gathering for water resources.
- · Environmental monitoring agencies
- · Robotics and AI developers
- · Water resource management firms
- · Coastal regions and economies
- · Traditional, human-intensive surveying methods
- · Less efficient or less autonomous monitoring technologies
More precise and continuous data on river plume dynamics, informing better water quality control and ecological preservation efforts.
Expansion of similar multi-agent AI and robotics applications to other critical environmental monitoring tasks, such as marine biodiversity or pollution tracking.
Enhanced AI-driven environmental intelligence contributing to more resilient infrastructure planning and climate adaptation strategies in water-stressed regions.
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